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Validity and Reliability of a Conceptual Framework on Enhancing Learning for Students via Kinect: A Pilot Test
No full textTraditional method of teaching poses two significant problems – not all students learn alike, and the physical interaction needed poses health risk during pandemic. As such, for these students, an alternative learning method such as those that uses natural user interface (NUI) can be considered. This method would be beneficial for kinesthetic type learners and can be conducted remotely. The alternative learning program is a complementary method, thus still incorporates the current subject syllabus. However, the delivery, learning and execution of the syllabus will be varied. In minimizing these gaps found in the current Malaysian education system, a conceptual framework utilizing Microsoft Kinect is proposed. Since this is a new framework, a pilot study is needed to gauge the validity and reliability of the survey instrument prior to embarking on further study on the outcome of the alternative learning program. Face and content validity conducted on the questionnaire were found to be clear, not confusing, and measures what the questions were supposed to measure. Reliability measured using Cronbach’s Alpha indicated values above the acceptable range. Thus, these results indicate that the instrument is valid and reliable to be applied for data collection in the future study to assess the intention of Malaysian students to adopt an alternative medium for learning.
Manuscript received: 22 Dec 2023 | Revised: 13 Jan 2024 | Accepted: 13 Feb 2024 | Published: : 30 Apr 202
Review on Present-day Breast Cancer Detection Techniques
No full textBreast cancer remains a prevalent health complication among the female population. Early and reliable detection in an individual is necessary for effective treatment. Thus, R&D into techniques for detection of breast cancer continues to the present. Non-invasive techniques include tactile examinations, electromagnetic scanning and checks for chemical markers. Invasive techniques include biopsies that extract tissue and liquid samples. These techniques have limitations and setbacks that are being addressed with supplementary or complementary techniques. Like the pre-existing techniques, these techniques also rely on comparison of data between control samples and afflicted patients to measure their reliability. Therefore, R&D efforts towards detection of breast cancer have resulted in incremental improvements on established methodologies.
Manuscript received: 25 Dec 2023 | Revised: 23 Feb 2024 | Accepted: 13 Mar 2024 | Published: : 30 Apr 202
Human Fall Motion Prediction – A Review
No full textAbstract – In predicting human fall motion, focused on enhancing safety and quality of life for the elderly and individuals at risk of falls. By highlighting the critical role of Human Pose Estimation, advancements in human motion forecasting, and fall prediction. It explores the continuous efforts to improve fall detection systems using innovative technologies, such as wearable sensors and IoT devices to implement deep learning models and analyze human poses and gestures. Various methods show promise in accurately predicting human fall motion by capturing complex patterns and relationships in the data. For instance, self-attention mechanisms can revolutionize human motion prediction by effectively capturing these intricate patterns, leading to more accurate predictions. Future research directions should focus on enhancing model accuracy, exploring new techniques for capturing complex patterns, and enabling real-time implementation in wearable devices or smart environments. By addressing these areas, fall detection systems can be significantly improved, benefiting individuals and healthcare systems worldwide.
Manuscript received: 15 Apr 2024 | Revised: 20 June 2024 | Accepted: 12 July 2024 | Published: : 30 Sep 202
A Qualitative Study of Factors That Influence Entrepreneurial Intention Among Students of Private Higher Education Institutions in Selangor: DOI: https://doi.org/10.33093/ijomfa.2024.5.1.10
No full textEntrepreneurship as a field of study has different sub-dimensions due to its usefulness in society, which makes it difficult to gain a holistic understanding of its key due to practical applications. A shift in generations creates a different yet unique business environment, especially in new business ventures. Younger generations have a different view and insight about entrepreneurship in small business. Thus, this study aims to seek deeper insight into entrepreneurial behaviour and intention among younger generations. Twenty semi-structured interviews with students from a higher private university in Malaysia were performed using the qualitative method. Using Nvivo 12, the interviews' transcriptions were used for content analysis to produce themes. This study highlights the four emerging themes from students’ perspectives: expectations for future goals, financial constraints in business venturing, personal growth, and opportunity cost to venture into business. The results of this study add to the body of knowledge in the field of entrepreneurship research since they emphasize students' intentions, worries, and considerations while starting a small firm. Additionally, it contributes to empirical research aimed at improving entrepreneurship ecosystems
Temporal Convolutional Recurrent Neural Network for Elderly Activity Recognition
No full textResearch on smartphone-based human activity recognition (HAR) is prevalent in the field of healthcare, especially for elderly activity monitoring. Researchers usually propose to use of accelerometers, gyroscopes or magnetometers that are equipped in smartphones as an individual sensing modality for human activity recognition. However, any of these alone is limited in capturing comprehensive movement information for accurate human activity analysis. Thus, we propose a smartphone-based HAR approach by leveraging the inertial signals captured by these three sensors to classify human activities. These heterogeneous sensors deliver information on various aspects of nature, motion and orientation, offering a richer set of features for more accurate representations of the activities. Hence, a deep learning approach that amalgamates long short-term memory (LSTM) in temporal convolutional network (TCN) is proposed. We use independent temporal convolutional networks, coined as temporal convolutional streams, to independently analyse the temporal data of each sensing modality. We name this architecture multi-stream TC-LSTM. The performance of multi-stream TC-LSTM is assessed on the self-collected elderly activity database. Empirical results exhibit that multi-stream TC-LSTM outperforms the existing machine learning and deep learning models, with an F1 score of 98.3 %.
Manuscript Received: 18 April 2024, Accepted: 10 June 2024, Published: 15 September2024, ORCiD: 0000-0002-3781-662
Admissibility of Non-Muslim’s Witness Statement in Syariah Court: A Comparison Between Nyonya Binti Tahir and Kaliammal a/p Sinnasamy
No full textIn January 2006, the landmark judgment made by the Syariah High Court of Seremban in the case of Nyonya Binti Tahir, Ex-Parte Majlis Agama Islam Negeri Sembilan that Nyonya Binti Tahir was no longer a Muslim at the time of her death and allowed her family to carry out her funeral. The decision was held after the Syariah High Court Judge considered thedeceased affidavit and the testimonies of the deceased children who are non-Muslims. The decision contrasted with one made in December 2005, Kaliammal a/p Sinnasamy v. Pengarah Jabatan Agama Islam Wilayah Persekutuan (JAWI), in which the Federal Territories of Syariah High Court determined that the deceased, Mount Everest climber Moorthy a/l Maniam, was a Muslim based on a military record, despite not having heard the testimony from his wife, Kaliammal. The Syariah Court of the Federal Territories refused to hear her testimony since she did not have any stand to testify in court. This case commentary critically analyses the rationale behind the Syariah High Court’s decision in the following case Nyonya Binti Tahir, Ex-Parte Majlis Agama Islam Negeri Sembilan in admitting non-Muslim testimony in the Syariah court
The Enhanced Speech Recognition in Automated Home Lighting System using Adaptive Time-Frequency Domain Noise Removal Algorithm Filter
No full textNumerous studies have explored speech recognition performance in Smart Home environments. However speech recognition accuracy diminishes when voice commands are captured in noisy areas of the home. This study aims to enhance speech recognition performance in such noisy environments. Instead of relying on remote control signals, a Bluetooth system is employed for short-range wireless communication to identify speech commands. Various sound levels are measured in decibels (dB) at different distances using the Smart Noise Application. A filter algorithm with Adaptive Filtering is used to minimize unwanted noise. The algorithm uses Adaptive Time-Frequency Domain Noise Removal (TFDNR) to mitigate background noise. Overall, the integrated system comprising Smartphone, Bluetooth, Arduino microcontroller, and noise detection software exhibits improved performance compared to previous studies, highlighting its potential for seamless smart home automation.
Manuscript received: 31 Dec 2023 | Revised: 25 January 20204 | Accepted: 26 Mar 2024 | Published: : 30 Apr 2024
Genetic Algorithm-Based Multitier Ensemble Classifier for Diagnosis of Heart Disease
No full textDesigning a hybrid or ensemble data mining system appropriate to the application is a research challenge. Heart disease is a life threatening disease that need to be recognized correctly in the starting stage before it becomes more complex. Using artificial intelligence techniques in a hybrid and ensemble architecture can support the prediction of heart disease more effectively based on the given sample cases. This paper proposes a classification system called genetic algorithm-based ensemble classification system (GA-ECS) for the identification of heart disease. As feature selection is the crucial step before applying the data mining techniques, the genetic algorithm is used in GA-ECS to identify the best features in a given dataset. The Cleveland heart disease dataset is used for testing GA-ECS. The performance of GA-ECS is compared with different machine learning classifiers for the prediction of heart disease. GA-ECS showed a promising outcome with an accuracy of 90% for the diagnosis of heart disease.
Manuscript received: 30 Nov 2023 | Revised: 15 Dec 2023 | Accepted: 12 Jan 2024 | Published: 30 Apr 202
Effect of Social Media Influencers on Generation Y Purchase Intention: Evidence From Men’s Skincare Products In Malaysia: DOI: https://doi.org/10.33093/ijomfa.2024.5.1.5
No full textAlthough the demand for men’s skincare products is becoming increasingly popular in Malaysia, previous studies of social media marketing have shed little light on the effect of social media influencers on men’s skincare purchase intention. Therefore, this research investigates the effects of social media influencers’ attractiveness, trustworthiness, expertise, likability, similarity, and familiarity on purchase intention in men’s skincare products among Generation Y in Malaysia. An online questionnaire was implemented, and 279 respondents were gathered using the snowball sampling technique. After obtaining the desired responses, multiple regression analysis was adopted to examine the research model and hypotheses. The findings showed that social media influencers’ attractiveness, similarity, and familiarity positively affect the purchase intention of men’s skincare products. However, this paper found that the effects of social media influencers’ trustworthiness, expertise, and likability on purchase intention of men’s skincare products among Generation Y in Malaysia are insignificant. The research findings contribute to the current research literature by determining the six characteristics of social media influencers and purchase intention with new empirical insights from Malaysia in the context of men’s skincare products
A Conceptual Framework for Acceptance of Autonomous Vehicle in Malaysia: DOI: https://doi.org/10.33093/ijomfa.2024.5.1.7
No full textMobility is evolving globally. Automated vehicle technology is consistently advancing with the development of Artificial Intelligence and information communication technology. The Autonomous Vehicle is acknowledged with the benefits of reducing traffic fatalities, reduced emissions and convenience. If autonomous vehicles are widely adopted, the Sustainable Development Goals could be achieved. Various studies have been conducted to investigate the psychological factors (internal) as well as assess the efforts of institutions (external) in promoting the adoption of autonomous vehicles. Nonetheless, there is very few studies examined the impacts of internal and external factors on the acceptance of autonomous vehicles simultaneously. Therefore, this study is taken to close the gap in understanding the acceptance of autonomous vehicles in Malaysia by integrating the internal factors and external factors in a model. By reviewing the past studies, a conceptual framework which can offer a comprehensive insight to the policymakers and car makers from the public’s perspective is proposed. Implications from this study can serve as a basis for prioritising the budget resources and development guidelines for the successful implementation of autonomous vehicles in Malaysia